Private AI On Your Own Infrastructure: A Practical Guide to Self-Hosted LLMs

The pitch for a private, self-hosted language model is simple: your prompts, your documents, and your data never leave infrastructure you control. The execution is less simple. Here's what actually matters when evaluating it.

Why self-host in the first place

Sending internal documents, customer data, or proprietary code to a third-party AI API means that data leaves your security boundary — subject to that provider's retention policy, their subprocessors, and their incident history, not yours. For regulated industries or anyone handling material non-public information, that's often a non-starter regardless of how good the model is.

Self-hosting doesn't just mean privacy — it means the model's behavior, uptime, and cost are entirely under your control, with no rate limits set by someone else's business priorities.

Hardware: the real constraint

The dominant cost in self-hosted LLM infrastructure is GPU memory (VRAM), not raw compute. A model's parameter count roughly determines how much VRAM it needs to run:

• A 7-8B parameter model comfortably fits on a single consumer GPU with 16-24GB VRAM, quantized to 4-8 bits
• A 30-70B parameter model needs either a high-end workstation GPU (48GB+) or multiple GPUs, unless heavily quantized
• Anything larger moves into multi-GPU server territory — which is where the economics start to favor a smaller, well-chosen model over the largest one available

Quantization (running the model at reduced numerical precision — 8-bit or 4-bit instead of the original 16-bit) trades a small amount of output quality for a large reduction in memory footprint, and for most business use cases the difference is not noticeable.

Choosing a model: bigger isn't automatically better

Open-weight models (Llama, Mistral, Qwen, and others) now cover a wide range of sizes and specializations. For most internal tools — document Q&A, summarization, internal search, drafting assistance — a well-tuned mid-size model (7-30B parameters) running on hardware you already own often outperforms a much larger model in practice, because it's fast enough to actually get used and cheap enough to run continuously.

The largest available model is rarely the right default. Start with the smallest model that handles your actual use case acceptably, and only scale up if there's a concrete quality gap.

Serving the model: don't reinvent this part

Running inference efficiently — batching requests, managing memory, exposing an API — is a solved problem. Tools like vLLM, Ollama, and text-generation-inference handle this well and are actively maintained; building a custom inference server from scratch is rarely a good use of engineering time.

The right tool depends on scale: Ollama is the fastest path to a working local setup for smaller deployments; vLLM is built for higher-throughput, concurrent serving once you have real traffic to handle.

Data isolation is the point — design for it explicitly

Self-hosting only delivers on its privacy promise if the deployment actually keeps data internal end to end. That means:

• No default telemetry or "usage improvement" callbacks phoning home to a model provider
• Retrieval-augmented generation (RAG) pipelines — where the model searches your internal documents — need the vector database and document store to sit inside the same trust boundary as the model itself
• Access to the model's API should be authenticated and scoped like any other internal service, not left open on the assumption that "it's internal only"

The tradeoff to be honest about

Self-hosted models require real infrastructure investment and ongoing maintenance — driver updates, model updates, capacity planning — that a hosted API abstracts away entirely. The right choice depends on what you're optimizing for: convenience and always-current capability favor a hosted API; data control, predictable cost at scale, and independence from a provider's roadmap favor self-hosting.

For any organization where "our data touched a third party's servers" is itself the risk to eliminate, that tradeoff resolves quickly in favor of owning the infrastructure.